Despite the allocation of significant resources, the genomic basis of congenital heart disease (CHD) remains largely unknown in that rare and de novo single nucleotide and copy-number variants account for a small fraction of CHD. We hypothesize that (1) rare incompletely dominant, simple recessive, and compound- heterozygote disease models account for a very significant fraction of the missing heritability o CHD and (2) the genomic load of deleterious variants, together with pleomorphies associated with some CHD-causing alleles (but not others), influence clinical outcomes in a manner that transcends their immediate contribution to the primary CHD. Our principal goal is to establish the Utah Center as an integral partner of the Pediatric Cardiac Genomics Consortium (PCGC) and to work collaboratively with other PCGC centers in order to (1) provide the PCGC with the patients, expertise and software that will identify the missing heritability of CHD; and (2) associate genomic variants with relevant clinical outcomes by defining the pleomorphies associated with CHD-causing alleles and by determining the genomic load of deleterious variants. Currently, the PCGC is lacking two key diagnostic approaches that hinder its ability to define the genomic basis for CHD and its outcomes: (1) a robust bioinformatics pipeline that is capable of computing on incompletely dominant, simple recessive, and compound-heterozygote disease models, and (2) a family-based whole-genome sequencing approach that is powered to identify novel CHD alleles in coding and non-coding regions. We argue that the identification of de novo variants in PCGC proband-parent trios represents only the tip of the iceberg, with many genes and alleles still undiscovered. This proposal encompasses innovative methodologies that will benefit the PCGC in a very practical manner. This proposal capitalizes on a recently validated and emerging bioinformatics technology that provides four basic functionalities: (1) the ability to estimate the functional impact of variants no matter where they lie in the genome coding, intergenic, splice sites, etc.; (2) the ability to computationally interrogate patient genotypes not only for dominant de novo alleles, but also for incompletely penetrant dominant, simple recessive, and compound heterozygote disease models; (3) the ability to carry out these analyses in the context of pedigrees, phenotype information, and expression data from patient-specific induced pluripotent stem cell-derived cardiomyocytes; and (4) the capability to share results for patient management and consortium-wide collaborative analyses. In summary, our proposal aims to fill the knowledge gap of missing heritability surrounding CHD by leveraging novel tools designed at the University of Utah that enable integrated computation on personal genome/exome sequences, patient phenotype descriptions and pedigrees, and patient-specific expression data, all in a robust statistical framework.